The goal of this work is to robustly identify common brain networks and their corresponding temporal dynamics across subjects in asynchronous task functional MRI (tfMRI) signals. We approached this problem using a robust and scalable tensor decomposition method combined with the BrainSync algorithm. We first used BrainSync algorithm to temporally align asynchronous tfMRI data, allowing us to study common brain networks across subjects. We mapped the synchronized tfMRI data into a 3D tensor (vertices × time × session) and performed a greedy canonical polyadic (CP) decomposition, reducing the rank to 20 in order to improve the signal-to-noise ratio (SNR). We incorporated the Nesterovaccelerated adaptive moment estimation into our previously developed scalable and robust sequential CP decomposition (SRSCPD) framework and applied this improved version of SRSCPD to the rank-reduced tensor to identify dynamic brain networks. We successfully identified 9 brain networks with their corresponding temporal dynamics from 40 subjects using Human Connectome Project tfMRI data without using any prior information with regard to the task designs. Three of these show the subjects’ responses to cues at the beginning of each task block (fronto-parietal attentional control network, visual network and executive control network); one corresponds to the default mode network that exhibits deactivation during the tasks; four show motors networks (left hand, right hand, tongue, and both feet) where the temporal dynamics are strongly correlated to the task designs, and the remaining component reflects physiological noise (respiration).